Method and a system for optimizing the configuration of multisite transcranial current stimulation and a computer-readable medium

ABSTRACT

A system and a method for optimizing the configuration of multisite transcranial current stimulation, including providing an electric field characteristic target map on the brain&#39;s cortex, the target map including multiple cortical targets, the multiple cortical targets are localized and/or continuously varying and spatially extended, providing a weight map on the cortical surface prioritizing the important of areas in the target map for the purposes of optimization; and calculating, based on the target and weight maps, optimal currents and optimal locations for a plurality of electrodes intended for providing transcranial current stimulation to globally stimulate at once the multiple cortical targets with excitatory, inhibitory or neutral stimulation.

FIELD OF THE INVENTION

The present invention generally relates to a method and a system foroptimizing the configuration of multisite (i.e., using 2 or moreelectrodes at different scalp locations) transcranial currentstimulation, based on the provision of weighted target maps of thebrain's cortex, and the calculation therefrom of optimal currents andoptimal locations for a plurality of electrodes intended to globallystimulate at once multiple cortical targets with excitatory, inhibitoryor neutral stimulation.

The present invention also relates to a computer-readable mediumcontaining program instructions for a computer to perform a method foroptimizing the configuration of multisite transcranial currentstimulation.

BACKGROUND OF THE INVENTION

Transcranial current stimulation (tCS) is a noninvasive brainstimulation technique in which weak, constant or slowly varyingelectrical currents are applied to the brain through the scalp. tCSincludes a family of related noninvasive techniques including direct(tDCS), alternating (tACS) and random noise current stimulation (tRNS).These techniques use scalp electrodes with electrode current intensityto area ratios of about 0.3-5 A/m2 at low frequencies (typically <1 kHz)resulting in weak electric fields in the brain, with amplitudes of about0.2-2V/m. The neuromodulatory effect of these fields

(Antal et al., 2008; Nitsche and Paulus, 2001, 2000; Terney et al.,2008) have been confirmed in many laboratories. In a typical tDCSexperiment, a continuous current of 1-2 mA is applied for up to 20 minthrough two large stimulation electrodes (25-35 cm2). For therapeuticapplications, such as post-stroke rehabilitation (Khedr et al. (2013))or the treatment of depression (Loo et al. (2012)), tDCS is usuallyapplied daily for five days, during one or more weeks.

While tCS interventions typically focus on a single cortical target, itis widely recognized today that many behavioral manifestations ofneurological and psychiatric diseases are not solely the result ofabnormality in one isolated brain region but represent alterations inbrain networks (see, e.g., Fox et al. (2012c) and references therein).In this context, and provided a specification for the location and typeof stimulation effects is available, brain networks become the target ofneuromodulatory interventions. Advances in neuroimaging technology suchas positron emission tomography (PET), electroencephalography (EEG),magnetoencephalography (MEG) and resting-state functional connectivityMRI (rs-fcMRI) are allowing us to non-invasively visualize brainnetworks in humans with unprecedented clarity. In a parallel and timelydevelopment, technologies have become available today which enable theuse of more than two electrodes for stimulation (two is the minimumnumber for current stimulation), making possible true current-controlledmultisite stimulation of brain networks. Determining the idealconfiguration of a multi-electrode tCS system, however, is complicatedby the fact that transcranial brain stimulation effects are largelynon-local due to Ohmnic propagation effects. For this reason,optimization algorithms based on globally defined, cortical targetingdata are needed.

As an especially interesting example, the use of rs-fcMRI seed maps isherein discussed (Shafi et al. (2012); Fox et al. (2012c)) for definingcortically extended tCS targets. In contrast to traditional task-basedfMRI, resting state fcMRI examines correlations in spontaneousfluctuations in the blood oxygen level dependent (BOLD) signal in theabsence of any explicit input or output, while subjects simply rest inthe scanner (see, e.g., Buckner et al. (2013) and references therein). Aconsistent observation is that regions with similar functionalproperties, such as the left and right motor cortices, exhibit coherentBOLD fluctuations even in the absence of movement under restingconditions. Negative correlations (anti-correlations) between regionswith apparent opposing functional properties have also been observed(Fox et al. (2005)). Significant rs-fcMRI abnormalities have beenidentified across almost every major neurological and psychiatricdisease (for a review see Fox and Greicius (2010)), and differencesacross subjects in rs-fcMRI are reproducible across scanning sessionsand have been related to individual differences in anatomicalconnectivity and behavior.

One of the most valuable clinical uses of rs-fcMRI may be to predict howfocal brain stimulation will propagate through networks, thus informingthe ideal site for stimulation (Fox and Greicius (2010); Fox et al.(2012c)). Recently, Fox et al. (2012b) used rs-fcMRI to identifydifferences in functional connectivity between effective and lesseffective DLPFC stimulation sites (Fox et al. (2012c,a)). Significantdifferences in connectivity were seen with the subgenual cingulate (SG),a region repeatedly implicated in antidepressant response and aneffective DBS target (Mayberg et al. (2005); Drevets et al. (2008);Mayberg (2009)). Based on this finding, Fox et al. used rsfcMRI with theSG to identify theoretically optimal TMS target coordinates in the leftDLPFC (Fox et al. (2012b)). A similar strategy can be applied to otherneurological diseases with effective or potentially effective DBS sitesincluding Parkinson's disease, dystonia, essential tremor, Alzheimer'sdisease, and even minimally conscious state. An important challenge withthis approach is that rs-fcMRI with an effective DBS site does notidentify just a single cortical site, but many. In fact, it provides acontinuous pattern across the cortical surface of regions that are bothpositively and negatively correlated with the deep brain stimulationsite of interest. Realizing the full potential of this targetingapproach thus requires the ability to simultaneously excite or inhibitmultiple sites across the surface of the cortex. As will be seen below,the same occurs with targets from other imaging techniques, such as PET.While conventional TMS and tDCS technologies allow for only one or twostimulation sites, the multi-electrode approach perfectly complementsthis scientific and therapeutic need.

Next, some patent documents disclosing different proposals regarding theoptimization of the configuration of multisite transcranial currentstimulation are cited and briefly described.

U.S. Pat. No. 8,494,627 B2 discloses the automatic optimization ofdifferent parameters for multisite brain stimulation regarding anoptimal stimulation pattern (such as voltage, current, activation time,location, sequence or number of electrodes), based on a forward modelfor the brain tissue obtained using finite element model and taking intoaccount the brain response to different features, such as using aminimum number of electrodes, of current sources, giving a desiredorientation of induced electric fields/current density, considering theelectrical conductance as non-isotropic and or non-uniform, definingcertain constraints such as maximum allowable currents of fieldintensities at various tissue locations.

Different optimization criteria are disclosed in U.S. Pat. No. 8,494,627B2 formulated as a convex optimization problem and solved with a leastone of linearly constrained Least Squares minimization, weighted LeastSquares, Linearly Constrained Minimum Variance, maximum magnitude with alinear-norm constrains, or a convex optimization technique, although thescope of protection granted to said patent is limited to the optimizingof a first array of electrodes, the forming and posterior optimizing ofa second array of electrodes from the first array of electrodes, byremoving therefrom low current electrodes or electrodes with equalcurrent and opposite polarity.

Although it could be deduced from some portions of the disclosure ofU.S. Pat. No. 8,494,627 B2, that a cortical normal solution is sought,only concepts of electric fields radial and tangential to the skull todefine a target are used and disclosed in detail therein.

U.S. Pat. No. 8,494,627 B2 discloses injecting current at severaltranscranial locations in a controlled fashion, i.e. a multisitestimulation, but neither a multitarget stimulation, i.e. the use ofmultisite stimulation to induce electric fields at cortical locations asdetermined by the choice of one or more well-delineated (isolated)target locations in the cortex with an associated weighting scheme, noran extended cortical targeting and stimulation thereof, understood asthe use of multisite stimulation to induce electric fields in the entirecortex as specified by a cortical target map together with an associatedweight map, are disclosed in detail therein.

Chinese Patent Application Pub. No. CN102698360 also relates to theautomatic optimization of stimulation parameters for tDCS, and, withthat purpose, particularly discloses using a genetic algorithm takinginto account current distribution and spatial distribution and weightcoefficients, where the stimulation is a multichannel tDCS provided aplurality of channel electrodes of an electrode array, where eachchannel electrode has an independent control of the polarity and currentstrength delivered thereto.

CN102698360 does not either disclose a multitarget stimulation nor anextended cortical targeting, but only a multisite stimulation.

U.S. Patent Application Pub. No. US2013/0096363 describes methods,devices and systems for neuromodulation of deep brain targets using acombination of transcranial magnetic stimulation (TMS) and transcranialdirect current (DC) stimulation, where the latter is used only to reduceor eliminate side-effects, such as seizures, when modulating one or moredeep brain targets. In the specification of US2013/0096363 is statedthat although tDCS has been used in conjunction with TMS, the twotechniques have been applied only to cortical brain regions, and alsothat tDCS effectively only reaches the cortical surface of the brain,and not to elements of the brain which are not in contact with thesubdural pool of cerebral spinal fluid, because the spread of electricalcurrent depends upon this energy form passing through highly conductivemedia, thus not disclosing any indirect deep brain stimulation (DBS) tobe provided with the tDCS.

US2013/0096363 does not describe either a multitarget stimulation nor anextended cortical targeting.

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DESCRIPTION OF THE INVENTION

It is an object of the present invention to offer an alternative to theprior state of the art, with the purpose of providing a method and asystem which, contrary to the known methods and systems, really allowsthe optimal simultaneous stimulation of multiple cortical targets.

To that end, the present invention relates, in a first aspect, to amethod for optimizing the configuration of multisite transcranialcurrent stimulation, comprising:

-   -   providing a possibly signed (positive or negative) target map of        electric field characteristics on the brain's cortex, said        target map including multiple cortical targets, where said        multiple cortical targets are localized (i.e. well-delineated        isolated target locations in the cortex) and/or continuously        varying and spatially extended;    -   providing a weight map on the cortical surface prioritizing the        important of areas in said target map for the purposes of        optimization; and    -   calculating, based on said target and weight maps, optimal        currents and optimal locations for a plurality of electrodes        intended for providing transcranial current stimulation to        globally stimulate at once said multiple cortical targets with        excitatory, inhibitory or neutral stimulation, i.e., in order to        provide the above mentioned multitarget localized and/or        extended cortical stimulation.

For an embodiment, said multiple cortical targets are final targets.

For another embodiment of the method of the present invention, saidmultiple cortical targets are intermediary targets whose spatiallyextension patterns indirectly affect, via neuronal interaction, corticalor deeper targets in the brain.

The method comprises, for an embodiment, performing said calculation ofoptimal currents and electrode locations based on said spatiallyextension patterns and to their positive or negative correlation with adeep brain stimulation target.

Although an optimization based on cortical surface target maps could beseen as a limitation, in fact it isn't due to the rather large scale oftCS currents compared to grey matter thickness. However, if deeperstructures are sought, i.e. for said embodiment where the final targetis a deep brain stimulation target, a volume optimization problem can bedefined additionally.

According to an embodiment, said spatially extension patterns arespecific to a pathology and/or to a patient, hence the method of thepresent invention provides a transcranial stimulation personalized tosaid specific pathology and/or to said patient.

The above mentioned target and weight maps are obtained, according todifferent embodiments, from brain data obtained by means of a brainmonitoring technology, such as fMRI, rs-fcMRI, PET, EEG and MEG, or acombination thereof.

Preferably, the calculation of optimal currents and optimal electrodelocations is performed based on an optimization of several electricfield components as described by the target map, including electricfield distribution and orientation, and more preferably the target mapincludes the definition of targets based on a coordinate system relativeto the cortical surface, with targets for at least normal components ofrespective electric field vectors.

Conventionally, components of electric field vectors of transcranialbrain stimulation taken into consideration are radial or normal to theskull (see Dmochowski et al. (2011)), unlike to what is proposed by themethod of the present invention were, as stated above, components whichare normal to the cortical surface, i.e. to the cortex, are taken intoaccount, both for generating the target map and also for the posteriorstimulation of the mapped targets with such components normal to thecortex.

For a more elaborated embodiment, said target map further includestargets for tangential components, to the cortical surface, ofrespective electric field vectors, or more generally, of the fullelectric vector field.

According to an embodiment, said calculation of optimal currents andoptimal electrode locations is performed under constraints regarding atleast maximal electrodes number and maximal current at each electrodeand the total current injected into the brain by all electrodes at anytime.

The method comprises, for an embodiment, using a realistic head modeland electric field modeling to perform said optimization of severalelectric field components, such as a multilayer finite element model ofa realistic head, generic or specific to a patient, where said electricfield distribution and orientation is relative to the grey matter andwhite matter surfaces.

In order to increase focality for a cortical target, according to anembodiment, the above mentioned calculation of optimal currents andelectrodes optimal locations generates zero or near zero electric fieldvalues for those electrodes surrounding said cortical target ofincreased focality—as described by the target and weight maps.

Said plurality of electrodes are in a number above two andpreferentially above seven, and, for an embodiment, the plurality ofelectrodes are arranged according to an arbitrary EEG 10-20 or 10-10 orsimilar montage scheme with determined electrode positions, based on aset of pre-defined locations.

According to an embodiment, the method comprises using constrained leastsquares to optimize current intensities and a genetic algorithm tooptimize electrode number and electrode locations.

Said transcranial stimulation is at least one or a combination oftranscranial direct current stimulation, transcranial alternatingcurrent stimulation, transcranial random noise stimulation orstimulation with a more generic current waveform.

The present invention also relates, in a second aspect, to a system foroptimizing the configuration of multisite transcranial currentstimulation, comprising data processing means for:

-   -   providing a target map on the brain's cortex, said target map        including multiple cortical targets, where said multiple        cortical targets are localized and/or continuously varying and        spatially extended;    -   providing a weight map on the cortical surface prioritizing the        areas in said target map for the purposes of optimization; and    -   calculating, based on said target and weight maps, optimal        currents and optimal locations for a plurality of electrodes        intended for providing transcranial current stimulation to        globally stimulate at once said multiple cortical targets, or,        more generally, extended cortical patterns, with excitatory,        inhibitory or neutral stimulation.

The system of the second aspect of the present invention is adapted toimplement the method of the first aspect of the invention.

The present invention also relates, in a third aspect, to acomputer-readable medium (preferably non-transitory) containing programinstructions for a computer to perform the method for optimizing theconfiguration of multisite transcranial current stimulation of the firstaspect of the invention.

The mechanisms underlying the after-effects of tDCS are still thesubject of investigation, but in all cases these local changes arebrought about by the accumulated action of the applied electric fieldover time, directly or indirectly. For this reason, as explained above,the present invention is focused on electric field optimization.Moreover, given that that there are strong directional effects in theinteraction of electric fields and neurons, i.e., neurons are influencedmostly by the component of the electric field parallel to theirtrajectory (Ranck (1975); Rattay (1986); Rushton (1927); Roth (1994);Bikson et al. (2004); Fröhlich and McCormick (2010)), and that theeffects of tDCS depend on its polarity, knowledge about the orientationof the electric field is crucial in predicting the effects ofstimulation. The components of the field perpendicular and parallel tothe cortical surface are of special importance, since pyramidal cellsare mostly aligned perpendicular to the surface, while many corticalinterneurons and axonal projections of pyramidal cells tend to aligntangentially (Day et al. (1989); Fox et al. (2004); Kammer et al.(2007)). Thus, an important element in modeling is to provide theelectric field distribution and orientation relative to the grey matter(GM) and white matter (WM) surfaces (the latter might be important tostudy the possibility of polarizing corticospinal axons, theircollaterals and other projection neurons). In order to do this, asstated above, for an embodiment, a realistic head model derived fromstructural MRI images (Miranda et al. (2013)) to calculate the tCSelectric field components rapidly from, for example, arbitrary EEG 10-20montages is used in both, the method and the system of the presentinvention. Importantly, this modeling approach allows for fastcalculation of electric field components normal and parallel to the GMand WM surfaces.

The method and system of the present invention are intended foroptimizing the configuration of multisite transcranial currentstimulation of general, spatially extended cortical targets and, as willbe shown in a posterior section, how, based on fMRI, PET, EEG or otherdata specifying target and weight maps on the cortical surface forexcitatory, inhibitory or neutral stimulation and a constraint of themaximal number of electrodes, a solution can be produced with theoptimal currents and locations of the electrodes. The main features ofthe present invention, for different embodiments thereof, are:

a) the overall concept of working with extended, weighted corticalpattern target maps based on fMRI, PET, EEG, MEG or other data,

b) the emphasis on optimization of one or more electric field componentsas opposed to its magnitude or intensity,

c) the definition of targets based on a coordinate system relative tothe cortical surface, with targets for normal and tangential componentsof electric field, and

d) the use of advanced algorithms to optimize not only currents but alsothe number and location of electrodes given appropriate constraints.

For direct current tCS (tDCS) applications, some examples ofimplementation of this technique using an available tCS system providingup to 8 small Ag/AgCl stimulation electrodes are provided in a posteriorsection, where a demonstration of the implementation of the method bothfor localized and spatially extended targets defined using rs-fcMRI andPET data is given, with clinical applications in stroke and depression.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication with thecolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee.

The previous and other advantages and features will be better understoodfrom the following detailed description of embodiments, with referenceto the attached drawings, which must be considered in an illustrativeand non-limiting manner, in which:

FIG. 1: Montages for unilateral stroke treatment over the left motorcortex. Note the more centralized, “quasi-monopolar” nature of theelectric field impact area provided by the 8-electrode solution. Firstrow: target map. The color scale indicates the target value. Red areasare associated to negative electric field (cortical normal component)targets, while blue areas are associated to positive electric field(cortical normal component) targets. Second and third rows: corticalnormal electric field maps for a traditional (bipolar, where the twoelectrodes are depicted as big blue and red circles, respectively) 1 mAmontage vs. the 8-electrode (small blue and red circles) optimizedsolution (1 mA max, 4 mA total max) respectively. The color scale (fromblue to red) refers to the amplitude of the normal electric field at thecortical surface. Positive (red) values denote an inward directedcortical normal component of the electric field (in V/m). Fourth andFifth rows: Weighted Error maps (Err(x) in Equation 1 below) fortraditional and 8-electrode solutions respectively. Here, negativevalues (blue) indicate a better fit than no intervention, positivevalues (red) a worse fit.

FIG. 2: Montages for bilateral stroke treatment. Note the morecentralized nature of the electric field impact area with themulti-electrode solution. First row: target map over the motor cortex onboth hemispheres. The color scale indicates the target value. Red areasare associated to negative electric field (cortical normal component)targets, while blue areas are associated to positive electric field(cortical normal component) targets. Second and third rows: corticalnormal electric field maps for a traditional (bipolar) 1 mA montage vs.the 8-electrode optimized solution (1 mA max, 4 mA total max)respectively. The color scale (from blue to red) refers to the amplitudeof the normal electric field at the cortical surface. Positive (red)values denote an inward directed cortical normal component of theelectric field (in V/m). Fourth and Fifth rows: Weighted Error maps(Err(x) in Equation 1 below) for traditional and 8-electrode solutionsrespectively. Here, negative values (blue) indicate a better fit than nointervention, positive values (red) a worse fit.

FIG. 3: Montages for depression (from PET data). First row: target mapfrom PET changes in response to DBS therapy for depression. The colorscale indicates the target value. Red areas are associated to negativeelectric field (cortical normal component) targets, while blue areas areassociated to positive electric field (cortical normal component)targets. Second and third rows: cortical normal electric field maps fora traditional (bipolar) 1 mA montage vs. the 8-electrode optimizedsolution (1 mA max, 4 mA total max) respectively. The color scale (fromblue to red) refers to the amplitude of the normal electric field at thecortical surface. Positive (red) values denote an inward directedcortical normal component of the electric field (in V/m). Fourth andFifth rows: Weighted Error maps (Err(x) in Equation 1 below) fortraditional and 8-electrode solutions respectively. Here, negativevalues (blue) indicate a better fit than no intervention, positivevalues (red) a worse fit.

FIG. 4: Montages for depression (from SG rs-fcMRI seed target map).First row: target map. The color scale indicates the target value. Redareas are associated to negative electric field (cortical normalcomponent) targets, while blue areas are associated to positive electricfield (cortical normal component) targets. Second and third rows: normalelectric field maps for a traditional (bipolar) 1 mA montage vs. the8-electrode optimized solution (1 mA max, 4 mA total max) respectively.The color scale (from blue to red) refers to the amplitude of the normalelectric field at the cortical surface. Positive (red) values denote aninward directed cortical normal component of the electric field (inV/m). Fourth and Fifth rows: Weighted Error maps (Err(x) in Equation 1below) for traditional and 8-electrode solutions respectively. Here,negative values (blue) indicate a better fit than no intervention,positive values (red) a worse fit.

DETAILED DESCRIPTION OF SEVERAL EMBODIMENTS

In the present section it is shown how to use neuroimaging data tospecify a target map on the cortical surface for excitatory, inhibitoryor neutral stimulation, and how, given constraints on the maximal numberof electrodes and currents, a solution can be produced with the optimalelectrode currents and their, locations. The main differences of thepresent invention with other recent efforts stem from a) the overallconcept of working with extended, weighted cortical pattern target mapsbased on fMRI, PET, EEG, MEG or other data, b) the emphasis onoptimization of an electric field component as opposed to its magnitudeor intensity (as in, e.g., Sadleir et al. (2012)), c) the definition oftargets based on a coordinate system relative to the cortical surface,with targets for normal E^(⊥) and tangential E^(∥) components ofelectric field (as opposed to “radial or normal to the skull” as inDmochowski et al. (2011)), and d) the use of advanced algorithms tooptimize not only currents but also the number and location ofelectrodes given appropriate constraints. Finally, at the end of thissection the generalization of these methods to tACS is addressed,although in a more exploratory fashion.

Methods:

General Statement of the Problem:

The non-invasive stimulation problem can be loosely classified asfollows: a) single, isolated, localized target, b) bipolar or, moregenerally, multi-polar isolated/localized targets and c) corticalpattern targeting. With the single target case an issue that typicallyarises is how to deal with the return current, since the laws of physicsrequire current conservation and thus a minimum of two electrodes needto be applied. The return (or “reference”) electrode is normallypositioned in an area which is presumed not to play a role (e.g., “overthe contralateral orbit”), and sometimes it is chosen to have a largerarea than the “active” one so that its effects diffuse (Nitsche and etal. (2007)). More modern approaches include the so-called“high-definition tDCS”, where a return arrangement of electrodes isplaced close to the active electrode (see, e.g., Dmochowski et al.(2011) and references therein) or more general quasi-monopolar montagessuch as the one described below, which employ an array ofoptimally-placed return electrodes (see below in this section thedescription of the part referred as “Targeting localized corticalregions” and FIG. 1).

In bipolar or multi-polar targeting, two or more discrete targets areactually sought, some excitatory (anodal) and others inhibitory(cathodal) (as in, e.g., Ferrucci et al. (2009); Lindenberg et al.(2010); Mahmoudi et al. (2011); Chib et al. (2013)). This situation willnormally require the use of small electrodes, as electric fielddefocusing may be an issue if large electrodes are used. An example isprovided below (see below “Targeting localized cortical regions” andFIG. 2).

More generally, the possibility of global cortical targeting has beendesigned to achieve a more effective neuromodulatory outcome. In thecase of tDCS, such a map may just be a specification of the areas toexcite, inhibit, or leave unaffected, with a particular weighting mapfor each of them. Examples on the use of PET and rs-fcMRI generatedtarget maps are provided below (see “Cortical pattern target from PET”and “Cortical pattern target from rs-fcMRI”, respectively).

In the following, and without loss of generality, the discussion is madeconcrete by adopting the StarStim device specifications (NeuroelectricsBarcelona, Spain). This device provides up to 8 independentlycurrent-controlled stimulation electrodes (allowing for programmablelinear combinations of DC, AC or RNS currents at each electrode). Themaximal current at any electrode is 2 mA, while for safety the systemconstraints the maximal current injected into the brain by allelectrodes at any time to 4 mA. Stimulation electrodes (Ag/AgCl “Pi”electrodes, Neuroelectrics Barcelona, Barcelona, Spain) have a radius of1 cm and provide, through a gel interface, a contact area of π cm². Theelectrodes can be placed on a cap using an extension of the 10-20 systemproviding 27 default locations.

Realistic Head Model and Electric Field Modeling:

The electric field calculations described in the present section wereperformed using the realistic head model described in Miranda et al.(2013). Briefly, tissue boundaries were derived from MR images (scalp,skull, cerebrospinal fluid (CSF) including ventricles, Grey Matter andWhite Matter) and the Finite Element Method was used to calculate theelectric potential in the head, subject to the appropriate boundaryconditions. Tissues were assumed to be uniform and isotropic and valuesfor their electric conductivity were taken from the literature.

In order to compute electric fields rapidly, use of the principle ofsuperposition has been made. This states that with appropriate boundaryconditions, the solution to a general N-electrode problem can beexpressed as a linear combination of N−1 bipolar ones. A fixed referenceelectrode is first chosen, and then all the bipolar solutions using thiselectrode are computed. A general solution with an arbitrary number of Nelectrodes can then easily be computed as follows. The currents to beset can be described by an Nary array of the form [I₁, . . . , I_(N)],with the current conservation constraint I_(N)=Σ_(n=1) ^(N-1)I_(n). LetE_(n) be the electric field solution for a bipolar setup with currents[0 . . . +1 . . . −1] (in some chosen units, with the “+1” in the nthposition). For the general multi-electrode case, the electric field dueto currents [I₁ . . . I_(N)] is simply given by E=I₁E₁+ . . .+I_(N-1)E_(N-1).

In the present case, 27 Pi-electrodes were placed on the scalp at thepositions available in the standard StarStim cap. The electrodes wererepresented by cylindrical gel disks with a diameter of 1.0 cm and aheight of approximately 2.5 mm. Twenty six different calculations wereperformed, with the anode always at Cz and the cathode at one of theother 26 positions in the cap, with the current set to 1 mA. Theelectric field for each one of these bipolar montages was obtained asminus the gradient of the electric potential. The total electric fieldfor a given combination of bipolar montages is computed as the weightedvector sum of the electric field due to each montage. A comparison ofsuch superimposed solutions with the direct calculation showed that theerrors involved were completely negligible (<10⁻⁸ V/m). The electricfield distributions associated to traditional electrode montages withtwo 25 cm² circular sponge electrodes were also computed in order tocompare their performance to the optimized solutions.

In the convention used below, a positive value for the component of theelectric field normal to the cortical surface E^(⊥) means the electricfield component normal is pointing into the cortex. As is discussedbelow, such a field would be excitatory. On the other hand, an electricfield pointing out of the cortex (negative normal component) would beinhibitory.

Optimization Problem and Algorithms:

The basic mechanism for neuronal interaction in tCS is presently thoughtto arise from the coupling of electric fields to populations ofelongated neurons such as pyramidal cells (Roth (1994); Bikson et al.(2004); Radman et al. (2009); Rahman et al. (2013); Molaee-Ardekani etal. (2013); Ruffini et al. (2013) and references therein).Non-coincidentally, such populations are also recognized to be the maingenerators of EEG signals, in a process of spatially coherentoscillation at certain frequencies (see, e.g., Merlet et al. (2013) andreferences within). The role of other types of neurons (e.g.,interneurons such as basket cells) or other brain cells such as glia isnot well understood, since their distribution and connections arecomplex, but they are in principle less sensitive to such fields due totheir more isotropic structures and distributions. Nevertheless,according to this model, a necessary first step in modeling the effectsof tCS is to determine the spatial distribution of the generatedelectric fields in the brain.

At the single neuron level, the external electric field vector forcesthe displacement of intracellular ions (which mobilize to cancel theintracellular field), altering the neuronal ionic distribution andmodifying the transmembrane potential difference. For an ideal straightfinite fiber with space constant I and length L>>I in a locallyhomogeneous electric field {right arrow over (E)}, the transmembranepotential difference is largest at the fiber termination, with a valuethat can be approximated by I{right arrow over (E)}·{circumflex over(n)}, where {circumflex over (n)} is the unit vector parallel to theideal main fiber axis (see Ranck (1975); Ruffini et al. (2013); Rahmanet al. (2013) and references therein). This is essentially a first-orderTaylor approximation in the electric field, with a spatial scaleprovided by the membrane space constant”, and geometric directions byfield and fibre orientation. For short neurons of length L<I, thespatial scale factor tends to L. Thus, longer neurons with a highermembrane space constant will undergo a larger change in membranepotential.

Ideally, in order to define a montage optimization strategy it would benecessary to define the full target vectorial electric field (i.e., all3 components) values in the cortex or other areas. With such aspecification an optimization problem could easily be defined. However,this does not seem possible today. As proxies, desired target values forthe magnitude or some of the components of the electric field can bedefined. Working with magnitudes is a priori problematic, because themagnitude of the electric field vector or any of its components isinvariant under overall current reversal, and there is abundant evidenceshowing that, in general, current direction is an important parameter intDCS. Indeed, pyramidal neuron populations in the cortical outer layerdisplay a preferred alignment direction normal to the cortical surface.For this reason, they offer a clear target and preferred direction fortCS stimulation. While other electric field components may no doubt beimportant (Rahman et al. (2013)), it does not seem presently possible todetermine how to specify these components in any polarity sensitiveoptimization strategy, given the apparent isotropy of connections indirections other than the normal. For these reasons, and without loss ofgenerality, it has been chosen to focus here on the optimization of thecomponent of the electric field normal to the cortical surfaces.

With the fast electric field calculation algorithm in place, theoptimization problem is essentially defined by i) a target map on thecortical surface specifying the desired values for the electric field ateach point, ii) a weight map providing the degree of relative importanceof each location in the target map and, iii) a set of constraints on thenumber of electrodes and their currents, as described below in“Targeting localized cortical regions”.

The Target and Target Weight Maps

The target map can be a user-defined area or areas in the corticalsurface. Target maps can be defined ad-hoc by the user, or they can stemfrom, e.g., fMRI, PET, MEG or EEG data, as described above (“Generalstatement of the problem”). In the latter case techniques such asbandpass filtering and cortical mapping (a simpler version of EEGtomography where the generating dipoles are constrained on the corticalsurface) could be used to generate target maps (see the discussionbelow). Indeed, EEG connectivity analysis can be carried out at thevoxel or node level as opposed to electrode space (see, e.g., Ray et al.(2007)), providing a connectivity map similar to that in fcMRI.

The use of rs-fcMRI seed correlation t-test or statistical significancemaps (called here “t-maps”) is particularly appealing, as it can providelinks to deep regions not easily accessible by non-invasive stimulationtechniques. However, seed maps can also be used to target corticallocations and networks. Such applications may be of interest forpathologies such as stroke or epilepsy, with seeds defined by corticallesions. In this way, stimulation may not only directly target theaffected region, but the entire cortex exploiting network phenomena.

The algorithm described here as an example of the above considerationsstarts from the provision of a ternary choice: a given area may bestimulated for excitatory, inhibitory or neutral effects. Such choicesbasically define the targeted electric field normal component at eachregion. An electric field target value E₀ ^(⊥)(x) can be defined by theuser. Here a value based on the tCS literature (Miranda et al. (2013))has been used, where currents of the order of 1-2 mA are used. Forexample, E₀ ^(⊥)=+0.3 V/m is a reasonable target for excitation(electric field direction is defined to be positive here if directednormal and inwards at the cortical surface), E₀ ^(⊥)=−0.3 V/m forinhibition, and E₀ ^(⊥)=0 V/m for a neutral effect. The weights assignedto each location typically vary from 0 to 100, biasing the solutionstowards some specific targets areas. Such a target map is just anexample, since many other possibilities exist.

Current Intensity Optimization

Assuming that a set of electrode locations has been specified, wedescribe here the process of current intensity optimization given targetand weight maps. The generic system of equations to solve for ahypothetical N-electrode system is (for simplicity the ⊥ symbol used toindicate the normal component has been dropped) [E₁(x) . . .E_(N-1)(x)]·I=E₀(x), where E_(n)(x) is a basis function solution for aparticular bipolar combination (specifying the normal component of the Efield at each point x in the mesh), I the array of sought-for currents,and E₀(x) is the target value related to the t-map.

In the case of a statistical t-map target T(x) obtained from, e.g.,rs-fcMRI, moreover, a request is made that the equation associated toeach mesh point x be weighted by a weight W(x). If the statisticalsignificance t-map magnitude is large at a given cortical location, itis asked that the corresponding equation be enforced strongly, since thelocation under scrutiny is proportionally statistically significant.This can be implemented by multiplying each row in the target equationabove by W(x)=IT(x)I. In addition, if the target map at a given locationis not statistically significant it may be desired that the solution tohave no effect on it, that is, the target electric field for a givenlower threshold T_(min) should be set to 0. A minimum weight W_(min)should be set for such cases (e.g., W(x)=W_(min)=2). The t-testmagnitude chosen as lower threshold will depend on other statisticalaspects such as the number of subjects used in the creation of t-testmap from rs-fcMRI data.

The optimization problem is formalized using weighted least squares.Mathematically, the goal is to minimize the mean weighted errorχ(i)=Σ_(x) Err(x; I), where the error at each mesh point x is definedhere by (Equation 1)

$\begin{matrix}{{{Err}\left( {x;I} \right)} = \frac{\left( {{Y_{\omega}(x)} - {{E_{\omega}(x)}I}} \right)^{2} - \left( {Y_{\omega}(x)} \right)^{2}}{\left( \frac{1}{N_{x}} \right)\Sigma_{x}{W(x)}^{2}}} & (1)\end{matrix}$

Here, I are the currents, N_(x) the number of mesh points andY_(ω)(x)=E₀ T(x) if |T(x)|>T_(min), else Y_(ω)(x)=0, andE_(ω)(x)=E(x)W(x). Optimization is subject to the constraints|I_(n)|<I_(max) for n=1, . . . , N (with I_(N)=−Σ_(n=1) ^(N-1)I_(n)),where I_(max) is the maximal allowed current at any electrode, and Σ_(I)_(n) _(>0) I_(n)=(1/2)Σ_(N)|I_(N)|<I_(max) ^(T), where I_(max) ^(T) isthe maximal allowed total injected current into the brain.

Genetic Algorithm

Since in general it will be wished for practical reasons to limit thenumber of electrodes used, a search in the space of electrode locationsneeds to be carried out. Genetic algorithms (GAs) are often used tosolve directed search problems—as is the case here. Briefly, GAs imitatenature by treating candidate solutions to an optimization problem asindividuals endowed with a chromosome subject to evolution and naturalselection (for an introduction see, e.g., Mitchell (1998)). The geneticalgorithm implemented here is, in short, based on the definition of asolution by a “DNA” binary string (in this case of dimension N−1)specifying the electrodes to be used, and uses as optimization functionthe least squares error, i.e., the one with the best possible currentconfiguration for the chosen electrode locations. Cross-over andmutation functions are defined to ensure that the offspring of solutionsdo not violate the constraint of maximal number of electrodes in thesolution. Once a DNA string is specified (i.e., a particular montage),its fitness is easily computed by inverting the solution for thatparticular montage. Solutions with more than the maximal number ofelectrodes desired are penalized strongly. The algorithm, implementedwith specifically designed fitness, cross-over and mutation functions,converges rather quickly (in a few hours) and reliably to a solution.

The overall quality of the solution I is quantified by the mean weightederror χ(I) (note that χ=0 when all currents are set to zero). Anothergoodness-of-fit measure is provided by the related weighted crosscorrelation coefficient of target map and electric field,

$\begin{matrix}{{cc} = \frac{\Sigma_{x}{Y_{\omega}(x)}{{E_{\omega}(x)} \cdot I}}{\sqrt{\left. {{\Sigma_{x}\left( {Y_{\omega}(x)} \right)}^{2}{{\Sigma_{x}\left( {E_{\omega}(x)} \right)} \cdot I}} \right)^{2}}}} & (2)\end{matrix}$

a number between −1 and 1. In order to visually assess solution qualityas a quality map over the cortical surface, the error Err(x; I) can beused (as in the appended Figures).

Examples

Next some solutions using the above described technique are provided. InTable 1 a summary of the characteristics of each montage is provided,including a “full-cap” 27 channel solution. It can be observed thatincreasing the number of electrodes beyond 8 improves the performance ofthe solution only marginally for these particular target maps,especially the simpler ones.

TABLE 1 Montage comparisons for the four target maps discussed in thepresent section. Weighted Correlation Coefficient (WCC), mean weightederror χ(I) (mV2/m2), maximal current at any electrode and total injectedcurrent (μA) are provided for traditional (bipolar), 8 and 27 channelsolutions. Target Montage WCC χ(I) Max I Tot Inj I BA4 Left Traditional0.02 163 1,000 1,000  8 Channel 0.31 −8 1,000 1,297 27 Channel 0.31 −91,000 2,146 BA4 Bilateral Traditional −0.07 184 1,000 1,000  8 Channel0.26 −13 823 1,513 27 Channel 0.26 −14 854 2,045 rs-fcMRI SG seed mapTraditional 0.11 1 1,000 1,000  8 Channel 0.29 −214 1,000 3,262 27Channel 0.31 −239 1,000 4,000 PET DBS map Traditional −0.05 125 1,0001,000  8 Channel 0.21 −51 843 2,236 27 Channel 0.23 −59 1,000 4,000

Targeting Localized Cortical Regions:

As discussed above, in a typical tDCS study two electrodes are placed onthe scalp to target a specific brain region. The effect of the chosenmontage depends on the spatial distribution of the vectorial electricfield induced in the grey matter (GM) and white matter (WM), and sincein a bipolar montage the second electrode will carry the same amount ofcurrent as the primary electrode, undesired side effects may occur onthe “return” or “reference” site. Consider for example targeting theleft motor cortex for excitation, a common approach in strokerehabilitation (Mahmoudi et al. (2011)). Here the weights for the weightmap in the motor cortex areas are chosen to be twice as large as in therest of the cortex, where the field target is zero. In FIG. 1 asimulation of the electric field using a traditional montage with 25sq-cm sponges over C3 and FP2 (the contralateral orbit) is provided. Thewidespread nature of the induced fields can be observed, and theresulting high error as compared to the GA optimized 8 electrode montage(see Table 1 above). It can be noted that weighted cross-correlationcoefficients remain relatively low even for the best solutions,reflecting the limited freedom available to adapt to the requiredweighted target maps. Similarly, FIG. 2 illustrates a bipolar target mapused in stroke rehabilitation (e.g., Lindenberg et al. (2010); Mahmoudiet al. (2011)), with one excitatory target on the left motor cortex, theother (inhibitory) on the right. Again, the multi-electrode solutionprovides a superior fit, with better account for neutral effect targetareas.

Cortical Pattern Target from PET:

In FIG. 3 the solution for a cortical target map based on PET data(Mayberg et al. (2005)) is provided. The target reflects cerebral bloodflow (CBF) changes in response to deep brain stimulation therapy fortreatment resistant major depression. Accordingly, the optimizationproblem is designed (target map) to excite regions where CBF hasincreased, and inhibit regions where CBF decreases, with target weightsfor the weight map proportional to CBF change magnitude. As can be seenin Table 1, the multisite solution provides a better weighted error andcorrelation coefficient (Table 1) since it is able to “hit” the targetmap at several locations, while the classical montage performs ratherpoorly.

Cortical Pattern Target from Rs-fcMRI:

Continuing with the example of treatment of resistant major depression,an electrode montage that will excite and inhibit different areas ofcortex based on the cortical rs-fcMRI correlation statistical t-mappattern with the subgenual cingulate (SG) has been generated, withtarget weights proportional to t-map magnitude. In this case, thers-fcMRI t-map needs to be sign reversed to produce the target map,since the goal is inhibition of the associated seed. By excitinganti-correlated areas and inhibiting correlated areas, the presentinventors would hypothesize that this stimulation will propagate to andmaximally inhibit the SG, improving antidepressant response. Note thaton the basis of this target map there is no obvious rationale for usinga traditional montage with anodal stimulation over the left dorsolateralprefrontal cortex (DLPFC)—e.g., the rs-fcMRI target map is fairlysymmetric. In FIG. 4 the solution to this problem using an 8 electrodemontage as opposed to one using a traditional montage is provided, wherewe target the left DLPFC as depicted by the left Brodmann area BA46 (F3)with a return over Fp2 (see, e.g., Palm et al. (2012); Fregni et al.(2006)). Again, the multi-electrode solution yields a lower weightederror and higher correlation coefficient than the classical montage(Table 1).

Discussion:

The present invention provides a new method and new system foroptimization of tDCS montages with extended targets based on realistichead modeling of the components of the electric field as defined bycortical surfaces, which have been described above both from a moreconceptual view and from a more particular view (in this section). Theadvantage of working with the electric field on the cortical surface isthat is allows for optimization of the cortical surface normal (orperpendicular) component of the electric field, or of its tangentialcomponent, or, e.g., overall magnitude. The methodology is based oncurrent knowledge of the primary interaction of tCS electric fields andthe cortex. The optimization problem is defined in terms of a target mapwhich attributes weights to the different mesh points. This conceptmakes the method of the present invention very flexible and allows forworking with one or a few extended uniform targets with simple orarbitrary shapes or, more importantly, with extended targets weighted bysome measure of interest such as “activation” or “connectivity” obtainedusing various imaging modalities, with the ability of specifying thenumber of electrodes available for stimulation. As an example, focalityis achieved by prescribing zero field values at the nodes outside thetarget for which specific weights can also be specified. Safety inprotocol optimization is addressed by limiting the current through eachelectrode and the total current injected into the brain.

Target maps can be defined from various sources. These include fMRI,EEG—which raises the interesting possibility of closed-loop montageoptimization where EEG or fMRI data is used in real time to adjuststimulation parameters—positron emission tomograpy (PET) andnear-infrared spectroscopy (NIRS) (Shafi et al. (2012)). These brainimaging methods can be leveraged to provide information both forclinical or research applications. Magnetic resonance spectroscopy (MRS)can provide another potential means to gather additional, relevantneurochemical information that may help define whether excitatory orinhibitory stimulation should be applied to a given node. Diffusiontensor imaging (DTI) data could be used to refine electric field modelsto take into consideration conductivity anisotropy and also for definingvectorial (oriented) target maps beyond the cortical normal model.Furthermore, methods for aggregating information from these techniquesmay provide unique, yet insufficiently explored ways to further refinecortical target maps. Future efforts in this area would be valuable.

Even though the realistic simulation of electric fields in the brain isbased on solid physics, there is uncertainty on the precise conductivityvalues to be used. These limitations and others (including the use ofisotropic conductivity) in the realistic head modeling used here arediscussed in Miranda et al. (2013). Research is on-going on thesensitivity of electric fields to variability of conductivity variables.There is, nevertheless, a high need to contrast these models withmeasurements, certainly a topic for further work.

It is noted that the model used in the present section is based on thesingle-subject template Colin27. Other approaches can be envisioned,such as the use of the MNI-152 average model (Fonov et al. (2009)) or,even better, the use of personalized models based on individual scans,which will certainly be necessary in specific cases (e.g., the case ofdamaged brains or skulls). It is also noted that in the examples abovewe have used rs-fcMRI group data to define cortical maps. Target mapsmay eventually require individualization (e.g., individual differencesin rs-fcMRI associated to depression have been reported (Fox et al.(2012a)). However, while individualization in either case may add moreprecision, it is presently unclear in which cases the extra modelingeffort will be warranted, given that tCS fields are rather spatiallyspread. On the other hand, the normal component of the electric fieldpeaks mainly in the bottom of the sulci, and the main sulci are not toovariable among different subjects even though their position in thebrain can vary by a few centimeters. Similarly, the fact that targetsare generally distributed and large (the target maps usually display lowspatial frequencies) also means that the electric field is in effect“averaged over” the anatomy, making small anatomical details lessrelevant.

Finally, it is noted that the basic interaction model used in theembodiments described in the present section, where the effects ofstimulation are linearly depending on the electric vector field, may notbe accurate in all situations. In order to improve the obtained resultsfor said situations having said not so accurate stimulation effects,non-linear effects in electric field or dosage are taken into accountfor building and using a more complex and complete model, as it is knownthat they could play a role (e.g., the direction of the excitabilitychange has recently been shown to be intensity dependent (Batsikadze etal. (2013)).

Clinical research should explore this methodology in selectedinteresting applications to test its range of validity, with pilot testsin e.g., depression, Parkison's disease or stroke. Comparison of effectsusing traditional versus multifocal montages in healthy subjects wouldprovide an interesting starting point for such research.

Generalization to tACS:

The generalization of the proposed method to the case of tACS isnontrivial, even though the process for calculation of electric fieldsfor low frequencies (<1 kHz) is essentially the same as for tDCS (thesame applies to low frequency tRNS). That is, if E(x) is electric fieldthe solution to a DC current for a particular montage and currents, thenE(x, t)=E(x) cos(2πtf) is the solution to the analogous AC case in whicheach current is multiplied by cos(2πtf). The real difficulty here liesin the choice of a physiological meaningful optimization problem.

Current studies show that support of brain activity involves theorchestrated oscillatory activity of different and spatially separatedbrain regions (see, e.g., Buzsaki and Draguhn (2004); Buzsaki (2006)).Indeed, a major challenge for neuroscience today is to map and analyzethe spatio-temporal patterns of activity of the large neuronalpopulations that are believed to be responsible for informationprocessing in the human brain. Phase or amplitude synchronization mayrelate different functional regions operating at the same or differentfrequencies via cross-frequency synchrony. In principle, tACS ispotentially capable of acting on such natural rhythms in brain networksthrough the process of resonance (Fröhlich and McCormick (2010); Paulus(2011); Ruffini et al. (2013); Dayan et al. (2013); Antal and Paulus(2013)) and devices such as StarStim already allow for the simultaneousmultisite stimulation of different cortical regions with specificfrequencies and relative phases.

In order to configure properly a multisite monochromatic tACS montage(i.e., one using a single tACS frequency), EEG or MEG data can be usedto define the target frequency as well as a target cortical map. Thelatter could be obtained, e.g., using EEG tomography or cortical mappingalgorithms with EEG data filtered at the appropriate frequency band.

In addition, rs-fcMRI data can be used to define a tACS target map muchas discussed above. Although fMRI is capable of capturing relativelyslow metabolic changes, it has been shown to correlate with local fieldpotentials (LFPs) in the gamma range, and anti-correlate at slowfrequencies (Mukamel et al. (2005)). It would follow that there are twopossible scenarios. For tACS frequencies in the low frequency range (<25Hz), fMRI and LFP (and presumably. EEG) data anti-correlate, hence tACSwould be inhibitory with respect to the target map. In the highfrequency range (25-300 Hz), tACS would be expected to act in anexcitatory fashion. DC stimulation could be combined to target thecomplementary effect achieved by the chosen tACS frequency. E.g., forhigh frequency tACS, optimization could be defined by stimulation at theappropriate tACS frequency at the excitatory target map sites, with DCinhibitory stimulation at the complementary sites.

The next order of complexity will involve stimulation at different siteswith different frequencies. From the optimization point of view it wouldsuffice to provide target maps for each frequency—the generalization ofthe least-squares approach described below would be immediate by theprinciple of superposition (this time in the frequency domain)—with theerror function generalized as a weighted sum of error functions for eachfrequency component.

Going one step further, recent results using natural or even“endogenous” stimulation waveforms in vitro (which could be derived frompersonalized EEG in humans) are particularly intriguing (Fröhlich andMcCormick (2010)). While tCS technology allows for all thesepossibilities, research protocols need to be defined on solidneurophysiological hypotheses, given the large parameter space (whichincludes the number of electrodes, locations, current intensities andcurrent waveforms at each electrode).

A person skilled in the art could introduce changes and modifications inthe embodiments described without departing from the scope of theinvention as it is defined in the attached claims.

1. A method for optimizing the configuration of multisite transcranialcurrent stimulation, comprising: providing an electric fieldcharacteristic target map on the brain's cortex, said target mapincluding multiple cortical targets, where said multiple corticaltargets are localized and/or continuously varying and spatiallyextended; providing a weight map on the cortical surface prioritizingthe important of areas in said target map for the purposes ofoptimization; and calculating, based on said target and weight maps,optimal currents and optimal locations for a plurality of electrodesintended for providing transcranial current stimulation to globallystimulate at once said multiple cortical targets with excitatory,inhibitory or neutral stimulation.
 2. The method of claim 1, whereinsaid multiple cortical targets are final targets.
 3. The method of claim1, wherein said multiple cortical targets are intermediary targets whosespatially extension patterns indirectly affect, via neuronalinteraction, cortical or deeper targets in the brain.
 4. The method ofclaim 3, comprising performing said calculation of optimal currents andelectrode locations based on said spatially extension patterns and totheir positive or negative correlation with a deep brain stimulationtarget.
 5. The method of claim 1, wherein said spatially extensionpatterns are specific to a pathology and/or to a patient.
 6. The methodof claim 1, wherein said target map is obtained from brain activity dataobtained by means of a brain monitoring technology.
 7. The method ofclaim 6, wherein said brain monitoring technology is at least one offMRI, rs-fcMRI, PET, EEG and MEG, or a combination thereof.
 8. Themethod of claim 1, wherein said calculation of optimal currents andoptimal electrode locations is performed based on an optimization ofseveral electric field components, including electric field distributionand orientation.
 9. The method of claim 8, wherein said target mapincludes the definition of targets based on a coordinate system relativeto the cortical surface, with targets for at least normal components ofrespective electric field vectors.
 10. The method of claim 9, whereinsaid target map further includes targets for tangential components ofrespective electric field vectors.
 11. The method of claim 1, whereinsaid calculation of optimal currents and optimal electrode locations isperformed under constraints regarding at least maximal electrodes numberand maximal current at each electrode and the total current injectedinto the brain by all electrodes at any time.
 12. The method of claim 8,comprising using a realistic head model and electric field modeling toperform said optimization of several electric field components, wheresaid electric field distribution and orientation is relative to the greymatter and white matter surfaces.
 13. The method of claim 1, wherein inorder to increase focality for a cortical target, said calculationgenerates zero or near zero electric field values for those electrodessurrounding said cortical target of increased focality.
 14. The methodof claim 1, wherein said plurality of electrodes are in a number abovetwo and preferentially above seven.
 15. The method of claim 14, whereinsaid plurality of electrodes are arranged according to an arbitrary EEG10-20 or 10-10 or similar montage scheme with determined electrodepositions.
 16. The method of claim 12, comprising using constrainedleast squares to optimize current intensities and a genetic algorithm tooptimize electrode number and locations.
 17. The method of claim 12,wherein said realistic head model is a multilayer finite element modelof a realistic head, generic or specific to a patient.
 18. The method ofclaim 1, wherein said transcranial stimulation is at least one or acombination of transcranial direct current stimulation, transcranialalternating current stimulation, transcranial random noise stimulationor stimulation with a more generic current waveform.
 19. The method ofclaim 1, wherein said provision of said target and weight maps and saidcalculation of optimal currents and optimal electrodes locations areperformed automatically.
 20. A system for optimizing the configurationof multisite transcranial current stimulation, comprising dataprocessing means for: providing a target map on the brain's cortex, saidtarget map including multiple cortical targets, where said multiplecortical targets are localized and/or continuously varying and spatiallyextended; and providing a weight map on the cortical surfaceprioritizing the important of areas in said target map for the purposesof optimization; and calculating, based on said target and weight maps,optimal currents and optimal locations for a plurality of electrodesintended for providing transcranial current stimulation to globallystimulate at once said multiple cortical targets with excitatory,inhibitory or neutral stimulation.
 21. A computer-readable mediumcontaining program instructions for a computer to perform a method foroptimizing the configuration of multisite transcranial currentstimulation comprising: providing a target map on the brain's cortex,said target map including multiple cortical targets, where said multiplecortical targets are localized and/or continuously varying and spatiallyextended; providing a weight map on the cortical surface prioritizingthe important of areas in said target map for the purposes ofoptimization; and calculating, based on said target and weight maps,optimal currents and optimal locations for a plurality of electrodesintended for providing transcranial current stimulation to globallystimulate at once said multiple cortical targets with excitatory,inhibitory or neutral stimulation.